Traction-battery control in hybrid powertrain

ABSTRACT

A computer includes a processor and a memory storing processor-executable instructions. The processor is programmed to prevent a traction battery from providing power to a vehicle powertrain below a charge threshold, and then, upon one of (a) receiving an acceleration demand above an acceleration threshold and (b) predicting that a planned maneuver classified as high acceleration will occur within a time threshold, permit the traction battery to provide power to the vehicle powertrain below the charge threshold.

BACKGROUND

Hybrid-electric vehicles typically rely on two energy sources, aninternal-combustion engine and a battery with a motor/generator.Hybrid-electric vehicles typically rely on the combination of torquefrom the engine and torque from the motor to achieve driving dynamicsacceptable to occupants, e.g., accelerating sufficiently quickly from astandstill.

Autonomous and semi-autonomous vehicles include additional electronicscompared to nonautonomous vehicles. The additional electronics canimpose significant electrical loads on the electrical system of thevehicle. In a hybrid-electric vehicle, these electrical loads cansignificantly degrade drive performance, in particular, acceleration.Slower acceleration and worsened drive performance may require theautonomous operation to be designed to detect traffic and plan maneuversfurther in advance because of a slowed ability to react.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example vehicle.

FIG. 2 is a block diagram of an example propulsion of the vehicle ofFIG. 1.

FIG. 3 is a diagram of a state of charge of a traction battery of thepower system of FIG. 2.

FIG. 4 is a process flow diagram of an example process for controllingthe propulsion of FIG. 2.

DETAILED DESCRIPTION

The implementation of a power system described herein can provideimproved drive performance for a hybrid-electric vehicle despite theelectrical loads from, e.g., autonomous-vehicle electronics. A computeris programmed to prevent a traction battery from discharging to apowertrain in situations in which the traction battery typicallyotherwise would have discharged in a hybrid-electric vehicle, and thecomputer is also programmed to permit the traction battery to dischargein situations in which the extra power would most improve the driveperformance. The power system provides improved drive performance whilestill sufficiently powering the autonomous-vehicle electronics.

A computer includes a processor and a memory storingprocessor-executable instructions. The processor is programmed toprevent a traction battery from providing power to a vehicle powertrainbelow a charge threshold; and then, upon one of (a) receiving anacceleration demand above an acceleration threshold and (b) predictingthat a planned maneuver classified as high acceleration will occurwithin a time threshold, permit the traction battery to provide power tothe vehicle powertrain below the charge threshold.

The processor may be further programmed to instruct a generator tocharge the traction battery in response to a state of charge of thetraction battery falling below the charge threshold. The chargethreshold may be a first charge threshold, and the processor may befurther programmed to, after instructing the generator to charge thetraction battery, instruct the generator to cease charging the tractionbattery in response to the state of charge of the traction batteryincreasing above a second charge threshold.

The processor may be further programmed to prevent the generator fromstarting to charge the traction battery in response to the state ofcharge of the traction battery being above the charge threshold.

The processor may be further programmed to instruct anautonomous-driving computer of the vehicle to put the vehicle in aminimal risk condition in response to a state of charge of the tractionbattery falling below the charge threshold and a generator beingunavailable. Putting the vehicle in a minimal risk condition may bedriving the vehicle to a roadside.

A method includes preventing a traction battery from providing power toa vehicle powertrain below a charge threshold; and then, upon one of (a)receiving an acceleration demand above an acceleration threshold and (b)predicting that a planned maneuver classified as high acceleration willoccur within a time threshold, permitting the traction battery toprovide power to the vehicle powertrain below the charge threshold.

The method may further include instructing a generator to charge thetraction battery in response to a state of charge of the tractionbattery falling below the charge threshold. The charge threshold may bea first charge threshold, and the method may further include, afterinstructing the generator to charge the traction battery, instructingthe generator to cease charging the traction battery in response to thestate of charge of the traction battery increasing above a second chargethreshold.

The method may further include preventing the generator from starting tocharge the traction battery in response to the state of charge of thetraction battery being above the charge threshold.

The method may further include instructing an autonomous-drivingcomputer of the vehicle to put the vehicle in a minimal risk conditionin response to a state of charge of the traction battery falling belowthe charge threshold and a generator being unavailable. Putting thevehicle in a minimal risk condition may be driving the vehicle to aroadside.

A vehicle includes a vehicle powertrain, a traction battery electricallycoupled to the vehicle powertrain, and a computer. The computer isprogrammed to prevent the traction battery from providing power to thevehicle powertrain below a charge threshold; and then, upon one of (a)detecting an acceleration demand above an acceleration threshold and (b)predicting that a planned maneuver classified as high acceleration willoccur within a time threshold, permit the traction battery to providepower to the vehicle powertrain below the charge threshold.

The vehicle may further include a generator electrically coupled to thetraction battery, and the computer may be programmed to instruct thegenerator to charge the traction battery in response to a state ofcharge of the traction battery falling below the charge threshold. Thecharge threshold may be a first charge threshold, and the computer maybe further programmed to, after instructing the generator to charge thetraction battery, instruct the generator to cease charging the tractionbattery in response to the state of charge of the traction batteryincreasing above a second charge threshold.

The computer may be further programmed to prevent the generator fromstarting to charge the traction battery in response to the state ofcharge of the traction battery being above the charge threshold.

The vehicle may further include a generator electrically coupled to thetraction battery, and an autonomous-driving computer communicativelycoupled to the computer. The computer may be programmed to instruct theautonomous-driving computer to put the vehicle in a minimal riskcondition in response to a state of charge of the traction batteryfalling below the charge threshold and the generator being unavailable.Putting the vehicle in a minimal risk condition may be driving thevehicle to a roadside.

A computer 30 includes a processor and a memory storingprocessor-executable instructions. The processor is programmed toprevent a traction battery 32 from providing power to a powertrain 34 ofa vehicle 36 below a first charge threshold, and then, upon one of (a)receiving an acceleration demand above an acceleration threshold and (b)predicting that a planned maneuver classified as high acceleration willoccur within a time threshold, permit the traction battery 32 to providepower to the powertrain 34 below the first charge threshold.

With reference to FIG. 1, the vehicle 36 may be an autonomous orsemi-autonomous vehicle. An autonomous-vehicle computer 38 can beprogrammed to operate the vehicle 36 independently of the interventionof a human driver, completely or to a lesser degree. Theautonomous-vehicle computer 38 may be programmed to operate a propulsion40, brake system 42, steering system 44, and/or other vehicle systems.For the purposes of this disclosure, autonomous operation means theautonomous-vehicle computer 38 controls the propulsion 40, brake system42, and steering system 44 without input from a human driver;semi-autonomous operation means the autonomous-vehicle computer 38controls one or two of the propulsion 40, brake system 42, and steeringsystem 44 and a human driver controls the remainder; and nonautonomousoperation means a human driver controls the propulsion 40, brake system42, and steering system 44.

The autonomous-vehicle computer 38 is a microprocessor-based computer.The autonomous-vehicle computer 38 includes a processor, memory, etc.The memory of the autonomous-vehicle computer 38 includes memory forstoring instructions executable by the processor as well as forelectronically storing data and/or databases.

The autonomous-vehicle computer 38 may transmit and receive data througha communications network 46 such as a controller area network (CAN) bus,Ethernet, WiFi, Local Interconnect Network (LIN), onboard diagnosticsconnector (OBD-II), and/or by any other wired or wireless communicationsnetwork. The autonomous-vehicle computer 38 may be communicativelycoupled to the propulsion 40, the brake system 42, the steering system44, sensors 48, the computer 30, and other components via thecommunications network 46.

The sensors 48 may provide data about operation of the vehicle 36, forexample, wheel speed, wheel orientation, and engine and transmissiondata (e.g., temperature, fuel consumption, etc.). The sensors 48 maydetect the location and/or orientation of the vehicle 36. For example,the sensors 48 may include global positioning system (GPS) sensors;accelerometers such as piezo-electric or microelectromechanical systems(MEMS); gyroscopes such as rate, ring laser, or fiber-optic gyroscopes;inertial measurements units (IMU); and magnetometers. The sensors 48 maydetect the external world, e.g., objects and/or characteristics ofsurroundings of the vehicle 36, such as other vehicles, road lanemarkings, traffic lights and/or signs, pedestrians, etc. For example,the sensors 48 may include radar sensors, scanning laser range finders,light detection and ranging (LIDAR) devices, and image processingsensors such as cameras. The sensors 48 may include communicationsdevices, for example, vehicle-to-infrastructure (V2I) orvehicle-to-vehicle (V2V) devices.

The propulsion 40 of the vehicle 36 generates energy and translates theenergy into motion of the vehicle 36. In particular, the propulsion 40may be hybrid propulsion. The propulsion 40 may include the powertrain34 arranged in any hybrid manner, e.g., a series-hybrid powertrain (asshown in FIG. 2), a parallel-hybrid powertrain, a power-split(series-parallel) hybrid powertrain, etc. The propulsion 40 is describedin more detail below with respect to FIG. 2. The propulsion 40 caninclude an electronic control unit (ECU) or the like that is incommunication with and receives input from the autonomous-vehiclecomputer 38 and/or a human driver. The human driver may control thepropulsion 40 via, e.g., an accelerator pedal and/or a gear-shift lever.

The brake system 42 is typically a known vehicle braking subsystem andresists the motion of the vehicle 36 to thereby slow and/or stop thevehicle 36. The brake system 42 may include friction brakes such as discbrakes, drum brakes, band brakes, etc.; regenerative brakes; any othersuitable type of brakes; or a combination. The brake system 42 caninclude an electronic control unit (ECU) or the like that is incommunication with and receives input from the autonomous-vehiclecomputer 38 and/or a human driver. The human driver may control thebrake system 42 via, e.g., a brake pedal.

The steering system 44 is typically a known vehicle steering subsystemand controls the turning of wheels 54 of the vehicle 36. The steeringsystem 44 may be a rack-and-pinion system with electric power-assistedsteering, a steer-by-wire system, as are both known, or any othersuitable system. The steering system 44 can include an electroniccontrol unit (ECU) or the like that is in communication with andreceives input from the autonomous-vehicle computer 38 and/or a humandriver. The human driver may control the steering system 44 via, e.g., asteering wheel.

The computer 30 is a microprocessor-based computer. The computer 30includes a processor, memory, etc. The memory of the computer 30includes memory for storing instructions executable by the processor aswell as for electronically storing data and/or databases. The computer30 may be the same computer as the autonomous-vehicle computer 38, orthe computer 30 may be one or more separate computers in communicationwith the autonomous-vehicle computer 38 via the communications network46. As a separate computer, the computer 30 may be, e.g., one or moreelectronic control units or modules (ECU or ECM) such as ahybrid-powertrain control module and/or a battery-energy control module.

With reference to FIG. 2, the propulsion 40 includes the powertrain 34that transmits power from an engine 50, from the traction battery 32, orfrom both the engine 50 and the traction battery 32 to a transmission 52and ultimately to the wheels 54 of the vehicle 36. The engine 50 may bean internal-combustion engine and may contain cylinders that serve ascombustion chambers that convert fuel from a reservoir 56 to rotationalkinetic energy. A generator 58 may receive the rotational kinetic energyfrom the engine 50. The generator 58 converts the rotational kineticenergy into electricity, e.g., alternating current, and powers anelectric motor 64. A charger/inverter 60 may convert the output of thegenerator 58, e.g., the alternating current, into high-voltage directcurrent to supply the traction battery 32 and a converter 62. Thehigh-voltage electricity may be on the order of 400 volts. The converter62 may be a DC/DC converter, e.g., may receive high-voltage directcurrent from the charger 60 and/or the traction battery 32 and convertthe high-voltage direct current to low-voltage direct current. Thelow-voltage direct current may be, e.g., 12 volts or 48 volts. Theconverter 62 may exchange the low-voltage direct current with alow-voltage battery 66, and the converter 62 may supply the low-voltagedirect current to loads 68. The loads 68 may include the computer 30 andthe autonomous-vehicle computer 38, among other electronics andcomponents of the vehicle 36. The electric motor 64 may convert theelectricity from the generator 58 into rotational kinetic energytransmitted to the transmission 52. The transmission 52 transmits thekinetic energy via, e.g., a drive axle to the wheels 54, while applyinga gear ratio allowing different tradeoffs between torque and rotationalspeed.

The traction battery 32 is a high-voltage battery, e.g., on the order of400 volts. The traction battery 32 may be any type suitable forproviding high-voltage electricity for operating the vehicle 36, e.g.,lithium-ion, lead-acid, etc.

The charger/inverter 60 controls how much power is supplied from thetraction battery 32 to the generator 58 of the powertrain 34 based oncommands from the computer 30. For example, the computer 30 may transmita power limit to the charger/inverter 50 specifying, e.g., a potentialdifference between the traction battery 32 and the generator 58. A lowerpower limit, e.g., a lower potential difference, means less power issupplied by the traction battery 32. The computer 30 may prevent thetraction battery 32 from discharging power to the generator 58 bytransmitting a power limit of substantially zero, i.e., negligibly abovezero with respect to the operation of the vehicle 36, to thecharger/inverter 50.

With reference to FIG. 3, the traction battery 32 has a state of chargeSoC_(actual) that can vary between 0% (no remaining charge) and 100%(fully charged). A charge level SoC_(max) is the maximum state of chargeto which the traction battery 32 can be charged without damaging thetraction battery 32. A charge level SoC_(min) is the minimum state ofcharge to which the traction battery 32 can be discharged withoutdamaging the traction battery 32. The charge levels SoC_(max) andSoC_(min) are physical characteristics of the traction battery 32 andtypically will be specified, e.g., by a supplier of the traction battery32. A charge level SoC_(high) is a maximum state of charge to which thetraction battery 32 will be charged in normal operation. The chargelevel SoC_(high) may be chosen to provide a safety factor below thecharge level SoC_(max). A charge level SoC_(low) is a minimum state ofcharge to which the traction battery 32 will be discharged in normaloperation. The charge level SoC_(low) may be chosen to provide a safetyfactor above the charge level SoC_(min). A charge level SoC_(disable)will be explained with respect to a process 400 below.

FIG. 4 is a process flow diagram illustrating an exemplary process 400for controlling the propulsion 40. The memory of the computer 30 storesexecutable instructions for performing the steps of the process 400.

In general, blocks 405 to 430 of the process 400 illustrate that thecomputer 30 is programmed to prevent the traction battery 32 fromproviding power to the powertrain 34 below a first charge thresholduntil the computer 30 either receives an acceleration demand above anacceleration threshold or predicts that a planned maneuver classified ashigh acceleration will occur within a time threshold, and then permitthe traction battery 32 to provide power to the powertrain 34 below thefirst charge threshold. For the purposes of this disclosure, “providingpower below a charge threshold” means discharging a battery to a stateof charge below the charge threshold. The first charge threshold may bethe charge level SoC_(disable). Basically, the traction battery 32conserves power above at least the charge level SoC_(disable) duringnormal operation of the vehicle 36 and only provides additional powerfor events needing higher-than-typical acceleration, as described inmore detail below.

The process 400 begins in a block 405, in which the computer 30 receivesan acceleration demand. The acceleration demand is any instruction thatthe vehicle 36 accelerate, i.e., increase speed. The acceleration demandmay be a demand for instantaneous acceleration or for, e.g., averageacceleration or target speed over a period of time. The accelerationdemand may be specified in, e.g., units of acceleration such as metersper second squared (m/s²). The autonomous-vehicle computer 38 may sendthe acceleration demand to the computer 30. Alternatively, a humandriver may send the acceleration demand by, e.g., pressing on theaccelerator pedal.

Next, in a decision block 410, the computer 30 determines whether theacceleration demand is above the acceleration threshold. Theacceleration threshold may be chosen to be low enough thatevasive-driving maneuvers requiring acceleration are typically above theacceleration threshold but most nonevasive driving is below theacceleration threshold. For example, a set of evasive-driving maneuversmay be performed while measuring acceleration on a test track for eachtype of vehicle 36. If the acceleration demand is above the accelerationthreshold, the process 400 proceeds to a block 425.

If the acceleration demand is below the acceleration threshold, next, ina block 415, the computer 30 predicts one or more planned maneuvers forthe vehicle 36 within a time threshold. For the purposes of thisdisclosure, a “planned maneuver” is a driving operation of the vehicle36 that the autonomous-vehicle computer 38 is planning to perform buthas not yet performed. The time threshold may be chosen based on a timehorizon over which the autonomous-vehicle computer 38 is capable ofplanning a planned maneuver, or the time threshold may be chosen basedon a time period needed to connect the traction battery 32 to thepowertrain 34. The computer 30 may be the same as the autonomous-vehiclecomputer 38 and therefore predict the planned maneuvers by determiningthe planned maneuvers according to known autonomous-driving algorithms.Alternatively, the computer 30 may predict the planned maneuvers byreceiving the planned maneuvers from the autonomous-vehicle computer 38.

Next, in a decision block 420, the computer 30 determines whether any ofthe planned maneuvers are classified as high acceleration. A maneuvermay be classified as high acceleration based on, e.g., whether themaneuver requires accelerating, e.g., at all or over the accelerationthreshold, and must be completed within a short time window, i.e., atime window long enough for the next maneuver that the vehicle 36 willperform. For example, a lookup table may store acceleration minimums andcorresponding time-window maximums, and the planned maneuvers areclassified as high acceleration if the acceleration of the plannedmaneuver is higher than one of the acceleration minimums and has a timewindow lower than the time-window maximum paired with the accelerationminimum.

Acceleration Minimum (m/s²) Time-Window Maximum (s) 15 2 17.5 1.8 20 1.6. . . . . .Alternatively or additionally, maneuvers may be classified as highacceleration based on surveying experienced human drivers about whichmaneuvers they feel require higher-than-typical acceleration. Thehigh-acceleration maneuvers may be stored in, e.g., a lookup table, suchas shown below. A planned maneuver is classified as high acceleration ifthe planned maneuver is in the table.

High-Acceleration Maneuvers Accelerating on freeway onramp Turning leftin front of oncoming vehicle less than 200 feet away Changing lanes toavoid obstacle with trailing vehicle in new lane . . .Alternatively to a lookup table, maneuvers may be classified as highacceleration or not with a machine-learning algorithm such as a neuralnetwork. The algorithm may be created using known machine-learningtechniques using a training set containing maneuvers already classifiedas high acceleration and not high acceleration, such as by a survey ofexperienced human drivers. The algorithm may then be able to classifynot-already-classified maneuvers during operation of the vehicle 36according to similarity to high-acceleration or non-high-accelerationmaneuvers in the training set. If the planned maneuvers are not highacceleration, the process 400 proceeds to a block 430.

If the acceleration demand is above the acceleration threshold after thedecision block 410, or if the planned maneuvers are high accelerationafter the decision block 420, in the block 425, the computer 30 permitsthe traction battery 32 to provide power to the powertrain 34 below thefirst charge threshold, i.e., the charge level SoC_(disable). Forexample, the computer 30 may transmit a power limit to thecharger/inverter 60 that is ramping down to substantially zero as thestate of charge SoC_(actual) of the traction battery 32 approaches thecharge level SoC_(low) below the charge level SoC_(disable). Thecharger/inverter 60 then permits the traction battery 32 to discharge tothe generator 58 of the powertrain 34 according to the power limit. Thecomputer 30 may store a first lookup table relating the power limit tothe state of charge SoC_(actual) of the traction battery 32. The lookuptable may also include other inputs, such as temperature of the tractionbattery 32, as shown in the example table below.

Temperature SoC_(actual) 0° C. 10° C. 20° C. 30° C. 200 V 175 V 187 V193 V 196 V 150 V 125 V 135 V 142 V 147 V 100 V (SoC_(disable)) 85 V 90V 93 V 98 V 50 V (SoC_(low)) 1.01 V 1.2 V 1.3 V 1.32 VThe computer 30 may use the state of charge SoC_(actual) and thetemperature to look up a power limit and then transmit that power limitto the charger/inverter 60. In the lookup table, as the state of chargeSoC_(actual) approaches the charge level SoC_(low), the power limitapproaches zero. When the charge level SoC_(actual) is near the chargelevel SoC_(disable), the power limit is well above zero. After the block425, the process 400 proceeds to a block 435.

If the planned maneuvers are not high acceleration after the decisionblock 420, in the block 430, the computer 30 prevents the tractionbattery 32 from providing power to the powertrain 34. For example, thecomputer 30 may transmit a power limit to the charger/inverter 60 thatis ramping down to substantially zero as the state of chargeSoC_(actual) of the traction battery 32 approaches the first chargethreshold, i.e., the charge level SoC_(disable). The charger/inverter 60then permits the traction battery 32 to discharge to the generator 58 ofthe powertrain 34 according to the power limit. The computer 30 maystore a second lookup table relating the power limit to the state ofcharge SoC_(actual) of the traction battery 32. The lookup table mayalso include other inputs, such as temperature of the traction battery32, as shown in the example table below.

Temperature SoC_(actual) 0° C. 10° C. 20° C. 30° C. 200 V 175 V 187 V193 V 196 V 150 V 125 V 135 V 142 V 147 V 100 V (SoC_(disable)) 1.01 V1.2 V 1.3 V 1.32 VThe computer 30 may use the state of charge SoC_(actual) and thetemperature to look up a power limit and then transmit that power limitto the charger/inverter 60. In the lookup table, as the state of chargeSoC_(actual) approaches the charge level SoC_(disable), the power limitapproaches zero. The traction battery 32 is thus prevented fromdischarging to a state of charge SoC_(actual) below the charge levelSoC_(disable). After the block 430, the process 400 proceeds to theblock 435.

In general, in the blocks 435 to 465, the computer 30 begins chargingthe traction battery 32 after the state of charge SoC_(actual) of thetraction battery 32 falls below the first charge threshold and continuescharging until the state of charge SoC_(actual) exceeds a second chargethreshold. The hysteresis between the first and second charge thresholdsprevents the traction battery 32 from switching between charging anddischarging too frequently, which may degrade performance over time. Thefirst charge threshold is the charge level SoC_(disable). The chargelevel SoC_(disable) may be chosen so that sufficient power existsbetween the charge level SoC_(disable) and the charge level SoC_(min)for the vehicle 36 to perform a high-acceleration maneuver or perform aminimal risk condition (described below with respect to a block 470).The charge level SoC_(disable) is higher than what would be required fora safety factor above the charge level SoC_(min), i.e., is higher thanthe charge level SoC_(low). The second charge threshold is the chargelevel SoC_(high); in other words, the second charge threshold issubstantially as highly charged as the traction battery 32 can be withinnormal operation.

After the block 425 or the block 430, in the block 435, the computer 30determines the state of charge SoC_(actual) of the traction battery 32.The traction battery 32 may include a battery monitoring system or thelike, such as is known, for reporting the state of charge SoC_(actual).

Next, in a decision block 440, the computer 30 determines whether thestate of charge SoC_(actual) is below the first charge threshold, i.e.,the charge level SoC_(disable). If the state of charge SoC_(actual) isbelow the charge level SoC_(disable), the process 400 proceeds to adecision block 455.

If the state of charge SoC_(actual) is above the charge levelSoC_(disable), next, in a decision block 445, the computer 30 determineswhether the traction battery 32 is currently in the process of charging.In other words, the computer 30 determines whether the charging hasstarted (as performed in a block 460 below) and has not yet stopped (asperformed in a block 465 below). If the traction battery 32 is notcurrently charging, the process 400 proceeds to the block 465 below.

If the traction battery 32 is currently charging, next, in a decisionblock 450, the computer 30 determines whether the state of chargeSoC_(actual) is below the second charge threshold, i.e., the chargelevel SoC_(high). If the state of charge SoC_(actual) is above thecharge level SoC_(high), the process 400 proceeds to the block 465.

Next, if the state of charge SoC_(actual) is below the charge levelSoC_(high), or, after the decision block 445, if the state of chargeSoC_(actual) is below the charge level SoC_(disable), in the block 455,the computer 30 determines whether the generator 58 is available, i.e.,whether the generator 58 is currently capable of providing sufficientpower to both charge the traction battery 32 and continue operating thepowertrain 34 of the vehicle 36. The computer 30 may compare the powerneeded to both charge the traction battery 32 and perform a currentmaneuver or the planned maneuvers with the power output of the generator58. The power output of the generator 58 may be determined viaconventional means, e.g., by a sensor monitoring the output or by usinga prestored value and monitoring if the generator 58 is active, i.e.,has not malfunctioned. If the generator 58 is unavailable, the process400 proceeds to the block 470.

If the generator 58 is available, next, in the block 460, the computer30 instructs the generator 58, e.g., via the charger 60, to charge thetraction battery 32. The generator 58 may start to charge the tractionbattery 32 or may continue charging the traction battery 32. After theblock 460, the process 400 returns to the block 405 for the process 400to restart.

If the traction battery 32 is not currently charging, after the decisionblock 445, or if the state of charge SoC_(actual) is above the chargelevel SoC_(high), after the decision block 450, in the block 465, thecomputer 30 prevents the generator 58 from charging the traction battery32. If the generator 58 is currently charging the traction battery 32,the computer 30 instructs the generator 58 to cease charging thetraction battery 32. If the generator 58 is not currently charging thetraction battery 32, the computer 30 prevents the generator 58 fromstarting to charge the traction battery 32. After the block 465, theprocess 400 returns to the block 405 for the process 400 to restart.

If the generator 58 is unavailable, after the decision block 455, in theblock 470, the computer 30 instructs the autonomous-vehicle computer 38of the vehicle 36 to put the vehicle 36 in a minimal risk condition.According to the National Highway Traffic Safety Administration (NHTSA)and the Society of Automotive Engineers (SAE), “‘Minimal risk condition’means low-risk operating condition that an automated driving systemautomatically resorts to either when a system fails or when the humandriver fails to respond appropriately to a request to take over thedynamic driving task.” For example, the minimal risk condition may beinitiating a handover to the human driver or autonomously driving thevehicle 36 to a roadside, i.e., stopping the vehicle 36 outside activelanes of traffic. The autonomous-vehicle computer 38 may perform theminimal risk condition by using known autonomous-operation algorithms.After the block 470, the process 400 ends.

In general, the computing systems and/or devices described may employany of a number of computer operating systems, including, but by nomeans limited to, versions and/or varieties of the Ford Sync®application, AppLink/Smart Device Link middleware, the MicrosoftAutomotive® operating system, the Microsoft Windows® operating system,the Unix operating system (e.g., the Solaris® operating systemdistributed by Oracle Corporation of Redwood Shores, Calif.), the AIXUNIX operating system distributed by International Business Machines ofArmonk, N.Y., the Linux operating system, the Mac OSX and iOS operatingsystems distributed by Apple Inc. of Cupertino, Calif., the BlackBerryOS distributed by Blackberry, Ltd. of Waterloo, Canada, and the Androidoperating system developed by Google, Inc. and the Open HandsetAlliance, or the QNX® CAR Platform for Infotainment offered by QNXSoftware Systems. Examples of computing devices include, withoutlimitation, an on-board vehicle computer, a computer workstation, aserver, a desktop, notebook, laptop, or handheld computer, or some othercomputing system and/or device.

Computing devices generally include computer-executable instructions,where the instructions may be executable by one or more computingdevices such as those listed above. Computer executable instructions maybe compiled or interpreted from computer programs created using avariety of programming languages and/or technologies, including, withoutlimitation, and either alone or in combination, Java™, C, C++, Matlab,Simulink, Stateflow, Visual Basic, Java Script, Perl, HTML, etc. Some ofthese applications may be compiled and executed on a virtual machine,such as the Java Virtual Machine, the Dalvik virtual machine, or thelike. In general, a processor (e.g., a microprocessor) receivesinstructions, e.g., from a memory, a computer readable medium, etc., andexecutes these instructions, thereby performing one or more processes,including one or more of the processes described herein. Suchinstructions and other data may be stored and transmitted using avariety of computer readable media. A file in a computing device isgenerally a collection of data stored on a computer readable medium,such as a storage medium, a random access memory, etc.

A computer-readable medium (also referred to as a processor-readablemedium) includes any non-transitory (e.g., tangible) medium thatparticipates in providing data (e.g., instructions) that may be read bya computer (e.g., by a processor of a computer). Such a medium may takemany forms, including, but not limited to, non-volatile media andvolatile media. Non-volatile media may include, for example, optical ormagnetic disks and other persistent memory. Volatile media may include,for example, dynamic random access memory (DRAM), which typicallyconstitutes a main memory. Such instructions may be transmitted by oneor more transmission media, including coaxial cables, copper wire andfiber optics, including the wires that comprise a system bus coupled toa processor of a ECU. Common forms of computer-readable media include,for example, a floppy disk, a flexible disk, hard disk, magnetic tape,any other magnetic medium, a CD-ROM, DVD, any other optical medium,punch cards, paper tape, any other physical medium with patterns ofholes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip orcartridge, or any other medium from which a computer can read.

Databases, data repositories or other data stores described herein mayinclude various kinds of mechanisms for storing, accessing, andretrieving various kinds of data, including a hierarchical database, aset of files in a file system, an application database in a proprietaryformat, a relational database management system (RDBMS), etc. Each suchdata store is generally included within a computing device employing acomputer operating system such as one of those mentioned above, and areaccessed via a network in any one or more of a variety of manners. Afile system may be accessible from a computer operating system, and mayinclude files stored in various formats. An RDBMS generally employs theStructured Query Language (SQL) in addition to a language for creating,storing, editing, and executing stored procedures, such as the PL/SQLlanguage mentioned above.

In some examples, system elements may be implemented ascomputer-readable instructions (e.g., software) on one or more computingdevices (e.g., servers, personal computers, etc.), stored on computerreadable media associated therewith (e.g., disks, memories, etc.). Acomputer program product may comprise such instructions stored oncomputer readable media for carrying out the functions described herein.

In the drawings, the same reference numbers indicate the same elements.Further, some or all of these elements could be changed. With regard tothe media, processes, systems, methods, heuristics, etc. describedherein, it should be understood that, although the steps of suchprocesses, etc. have been described as occurring according to a certainordered sequence, such processes could be practiced with the describedsteps performed in an order other than the order described herein. Itfurther should be understood that certain steps could be performedsimultaneously, that other steps could be added, or that certain stepsdescribed herein could be omitted. In other words, the descriptions ofprocesses herein are provided for the purpose of illustrating certainembodiments, and should in no way be construed so as to limit theclaims.

Accordingly, it is to be understood that the above description isintended to be illustrative and not restrictive. Many embodiments andapplications other than the examples provided would be apparent to thoseof skill in the art upon reading the above description. The scope of theinvention should be determined, not with reference to the abovedescription, but should instead be determined with reference to theappended claims, along with the full scope of equivalents to which suchclaims are entitled. It is anticipated and intended that futuredevelopments will occur in the arts discussed herein, and that thedisclosed systems and methods will be incorporated into such futureembodiments. In sum, it should be understood that the invention iscapable of modification and variation and is limited only by thefollowing claims.

All terms used in the claims are intended to be given their plain andordinary meanings as understood by those skilled in the art unless anexplicit indication to the contrary in made herein. In particular, useof the singular articles such as “a,” “the,” “said,” etc. should be readto recite one or more of the indicated elements unless a claim recitesan explicit limitation to the contrary. The adjectives “first” and“second” are used throughout this document as identifiers and are notintended to signify importance or order. “Substantially” as used hereinmeans that a dimension, time duration, shape, or other adjective mayvary slightly from what is described due to physical imperfections,power interruptions, variations in machining or other manufacturing,etc.

The disclosure has been described in an illustrative manner, and it isto be understood that the terminology which has been used is intended tobe in the nature of words of description rather than of limitation. Manymodifications and variations of the present disclosure are possible inlight of the above teachings, and the disclosure may be practicedotherwise than as specifically described.

What is claimed is:
 1. A computer comprising a processor and a memorystoring processor-executable instructions, the processor programmed to:prevent a traction battery from providing power to a vehicle powertrainwhen a state of charge of the traction battery is below a chargethreshold; receive a planned maneuver from an autonomous-drivingalgorithm, the planned maneuver classified as a high-accelerationmaneuver; and then, upon predicting that the planned maneuver will occurwithin a time threshold, permit the traction battery to provide power tothe vehicle powertrain when the state of charge of the traction batteryis below the charge threshold; wherein the memory is storing a lookuptable of a plurality of acceleration minimums and a plurality oftime-window maximums paired respectively with the acceleration minimums,and the planned maneuver is classified as a high-acceleration maneuverif an acceleration of the planned maneuver is higher than one of theacceleration minimums and a time window for performing the plannedmaneuver is less than the one of the time-window maximums paired withthe one of the acceleration minimums.
 2. The computer of claim 1,wherein the processor is further programmed to instruct a generator tocharge the traction battery in response to the state of charge of thetraction battery falling below the charge threshold.
 3. The computer ofclaim 2, wherein the charge threshold is a first charge threshold, andthe processor is further programmed to, after instructing the generatorto charge the traction battery, instruct the generator to cease chargingthe traction battery in response to the state of charge of the tractionbattery increasing above a second charge threshold.
 4. The computer ofclaim 2, wherein the processor is further programmed to prevent thegenerator from starting to charge the traction battery in response tothe state of charge of the traction battery being above the chargethreshold.
 5. The computer of claim 1, wherein the processor is furtherprogrammed to instruct an autonomous-driving computer of the vehicle toput the vehicle in a minimal risk condition in response to the state ofcharge of the traction battery falling below the charge threshold and agenerator being unavailable.
 6. The computer of claim 5, wherein puttingthe vehicle in a minimal risk condition is driving the vehicle to aroadside.
 7. A method comprising: preventing a traction battery fromproviding power to a vehicle powertrain when a state of charge of thetraction battery is below a charge threshold; receiving a plannedmaneuver from an autonomous-driving algorithm, the planned maneuverclassified as a high-acceleration maneuver; then, upon predicting thatthe planned maneuver will occur within a time threshold, permitting thetraction battery to provide power to the vehicle powertrain when thestate of charge of the traction battery is below the charge threshold;and storing a lookup table of a plurality of acceleration minimums and aplurality of time- window maximums paired respectively with theacceleration minimums; wherein the planned maneuver is classified as ahigh-acceleration maneuver if an acceleration of the planned maneuver ishigher than one of the acceleration minimums and a time window forperforming the planned maneuver is less than the one of the time-windowmaximums paired with the one of the acceleration minimums.
 8. The methodof claim 7, further comprising instructing a generator to charge thetraction battery in response to the state of charge of the tractionbattery falling below the charge threshold.
 9. The method of claim 8,wherein the charge threshold is a first charge threshold, the methodfurther comprising, after instructing the generator to charge thetraction battery, instructing the generator to cease charging thetraction battery in response to the state of charge of the tractionbattery increasing above a second charge threshold.
 10. The method ofclaim 8, further comprising preventing the generator from starting tocharge the traction battery in response to the state of charge of thetraction battery being above the charge threshold.
 11. The method ofclaim 7, further comprising instructing an autonomous-driving computerof the vehicle to put the vehicle in a minimal risk condition inresponse to the state of charge of the traction battery falling belowthe charge threshold and a generator being unavailable.
 12. The methodof claim 11, wherein putting the vehicle in a minimal risk condition isdriving the vehicle to a roadside.
 13. A vehicle comprising: a vehiclepowertrain; a traction battery electrically coupled to the vehiclepowertrain; and a computer programmed to: prevent the traction batteryfrom providing power to the vehicle powertrain below when a state ofcharge of the traction battery is a charge threshold; receive a plannedmaneuver from an autonomous-driving algorithm, the planned maneuverclassified as a high-acceleration maneuver; and then, upon predictingthat the planned maneuver will occur within a time threshold, permit thetraction battery to provide power to the vehicle powertrain when thestate of charge of the traction battery is below the charge threshold;wherein a memory of the computer is storing a lookup table of aplurality of acceleration minimums and a plurality of time-windowmaximums paired respectively with the acceleration minimums, and theplanned maneuver is classified as a high-acceleration maneuver if anacceleration of the planned maneuver is higher than one of theacceleration minimums and a time window for performing the plannedmaneuver is less than the one of the time-window maximums paired withthe one of the acceleration minimums.
 14. The vehicle of claim 13,further comprising a generator electrically coupled to the tractionbattery, wherein the computer is programmed to instruct the generator tocharge the traction battery in response to the state of charge of thetraction battery falling below the charge threshold.
 15. The vehicle ofclaim 14, wherein the charge threshold is a first charge threshold, andthe computer is further programmed to, after instructing the generatorto charge the traction battery, instruct the generator to cease chargingthe traction battery in response to the state of charge of the tractionbattery increasing above a second charge threshold.
 16. The vehicle ofclaim 14, wherein the computer is further programmed to prevent thegenerator from starting to charge the traction battery in response tothe state of charge of the traction battery being above the chargethreshold.
 17. The vehicle of claim 13, further comprising a generatorelectrically coupled to the traction battery, and an autonomous-drivingcomputer communicatively coupled to the computer, wherein the computeris programmed to instruct the autonomous-driving computer to put thevehicle in a minimal risk condition in response to the state of chargeof the traction battery falling below the charge threshold and thegenerator being unavailable.
 18. The vehicle of claim 17, whereinputting the vehicle in a minimal risk condition is driving the vehicleto a roadside.
 19. The computer of claim 1, wherein the planned maneuveris classified as a high-acceleration maneuver if a type of the plannedmaneuver is listed in a second lookup table stored in the memory.